def suspend_test_phase_estimation_debug(self):
theta = 5*np.pi/16
n_qubits = 2
a_idx = 1
k = 2
circuit = QuantumCircuit(n_qubits)
psi = QuantumState(n_qubits) # |ancilla>|logical>
phi = 1.25/2
print('k={}, phi={} mod (np.pi)'.format(k, phi))
# Apply H to ancilla bit to get |+> state
circuit.add_H_gate(a_idx)
# Apply kickback phase rotation to ancilla bit
circuit.add_RZ_gate(a_idx, -np.pi * phi)
# Apply C-U(Z0)
theta_k = 2 ** (k-1) * theta
print('phase:{} mod (np.pi)'.format(theta_k/np.pi))
circuit.add_RZ_gate(0, -theta_k)
circuit.add_CNOT_gate(a_idx, 0)
circuit.add_RZ_gate(0, theta_k)
circuit.add_CNOT_gate(a_idx, 0)
# Apply H to ancilla bit to get |+> state
circuit.add_H_gate(a_idx)
# run circuit
circuit.update_quantum_state(psi)
print(psi.get_vector())
# partial trace
p0 = psi.get_marginal_probability([2, 0])
p1 = psi.get_marginal_probability([2, 1])
print(p0, p1)
def visualize_probability(self):
state = QuantumState(self.G.n_qubit)
self.G.ansatz.update_quantum_state(state)
measured_list = [
list(map(int, list(format(x, f"0{self.G.n_qubit}b"))))
for x in range(2**self.G.n_qubit)
]
prob_list = []
for lis in measured_list:
prob_list.append(state.get_marginal_probability(lis))
plt.plot(range(2**self.G.n_qubit), prob_list)
def cost_phf_sample_oneshot(print_level, qulacs_hamiltonianZ, qulacs_s2Z,
qulacs_ancZ, coef0_H, coef0_S2, kappa_list):
"""Function:
Test function for sampling Hamiltonian and S** expectation values
with PHF just for once.
Author(s): Takashi Tsuchimochi
使われてない?
"""
t1 = time.time()
noa = Quket.noa
nob = Quket.nob
nva = Quket.nva
nvb = Quket.nvb
n_electrons = Quket.n_electrons
n_qubit_system = n_qubits
n_qubits = Quket.n_qubits + 1
anc = n_qubit_system
state = QuantumState(n_qubits)
circuit_rhf = set_circuit_rhfZ(n_qubits, n_electrons)
circuit_rhf.update_quantum_state(state)
circuit_uhf = set_circuit_uhfZ(n_qubits, noa, nob, nva, nvb, kappa_list)
circuit_uhf.update_quantum_state(state)
### Set post-measurement states ####
poststate0 = state.copy()
poststate1 = state.copy()
circuit0 = QuantumCircuit(n_qubits)
circuit1 = QuantumCircuit(n_qubits)
### Projection to anc = 0 or anc = 1 ###
circuit0.add_gate(P0(0))
circuit1.add_gate(P1(0))
circuit0.update_quantum_state(poststate0)
circuit1.update_quantum_state(poststate1)
### Renormalize each state ###
norm0 = poststate0.get_squared_norm()
norm1 = poststate1.get_squared_norm()
poststate0.normalize(norm0)
poststate1.normalize(norm1)
### grid loop ###
Ng = 4
beta = [-0.861136311594053, -0.339981043584856,
0.339981043584856, 0.861136311594053]
wg = [0.173927422568724, 0.326072577431273,
0.326072577431273, 0.173927422568724]
### a list to compute the probability to observe 0 in ancilla qubit
p0_list = np.full(n_qubits, 2)
p0_list[-1] = 0
### Array for <HUg>, <S2Ug>, <Ug>
samplelist = [5, 50, 500, 5000, 50000, 500000, 5000000]
Ng = 4
ncyc = 10
prints("", filepath="./log.txt", opentype="w")
for i_sample in samplelist:
sampleEn = []
sampleS2 = []
for icyc in range(ncyc):
prints(f"n_sample : {i_sample} ({icyc} / {ncyc})",
filepath="./log.txt")
HUg = []
S2Ug = []
Ug = []
Ephf = S2 = Norm = 0
for i in range(Ng):
### Copy quantum state of UHF (cannot be done in real device) ###
state_g = QuantumState(n_qubits)
circuit_rhf.update_quantum_state(state_g)
circuit_uhf.update_quantum_state(state_g)
### Construct Ug test
circuit_ug = QuantumCircuit(n_qubits)
### Hadamard on anc
circuit_ug.add_H_gate(anc)
controlled_Ug(circuit_ug, n_qubits, anc, np.arccos(beta[i]))
circuit_ug.add_H_gate(anc)
circuit_ug.update_quantum_state(state_g)
### Probabilities for getting 0 and 1 in ancilla qubit ###
p0 = state_g.get_marginal_probability(p0_list)
p1 = 1 - p0
### Compute expectation value <HUg> ###
HUg.append(sample_observable(state_g,
qulacs_hamiltonianZ,
i_sample).real)
### <S2Ug> ###
S2Ug.append(sample_observable(state_g,
qulacs_s2Z,
i_sample).real)
#S2Ug.append(qulacs_s2Z.get_expectation_value(state_g))
#Ug.append(p0 - p1)
Ug.append(sample_observable(state_g,
qulacs_ancZ,
i_sample).real)
### Norm accumulation ###
Norm += wg[i]*g[i]
sampleHUg[icyc, i] = HUg[i]
sampleS2Ug[icyc, i] = S2Ug[i]
sampleUg[icyc, i] = Ug[i]
#print(f"{p0=} {p1=} {p0-p1=}")
sampleHUg1.append(HUg[0])
sampleHUg2.append(HUg[1])
sampleHUg3.append(HUg[2])
sampleHUg4.append(HUg[3])
sampleS2Ug1.append(S2Ug[0])
sampleS2Ug2.append(S2Ug[1])
sampleS2Ug3.append(S2Ug[2])
SAMpleS2Ug4.append(S2Ug[3])
sampleUg1.append(Ug[0])
sampleUg2.append(Ug[1])
sampleUg3.append(Ug[2])
sampleUg4.append(Ug[3])
### Energy calculation <HP>/<P> and <S**2P>/<P> ###
Ephf = 0
for i in range(Ng):
Ephf += wg[i]*HUg[i]/Norm
S2 += wg[i]*S2Ug[i]/Norm
#print(f" E[PHF] = {Ephf} <S**2> = {S2} (Nsample = {i_sample})")
Ephf += coef0_H
S2 += coef0_S2
sampleEn[icyc, 0] = Ephf
sampleS2[icyc, 0] = S2
#print(f"(n_sample = {i_sample}): sample HUg1\n", sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n", sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n", sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n", sampleHUg4)
#print(f"(n_sample = {i_sample}): sample S2Ug1\n", sampleS2Ug1)
#print(f"(n_sample = {i_sample}): sample S2Ug2\n", sampleS2Ug2)
#print(f"(n_sample = {i_sample}): sample S2Ug3\n", sampleS2Ug3)
#print(f"(n_sample = {i_sample}): sample S2Ug4\n", sampleS2Ug4)
#print(f"(n_sample = {i_sample}): sample Ug1\n", sampleUg1)
#print(f"(n_sample = {i_sample}): sample Ug2\n", sampleUg2)
#print(f"(n_sample = {i_sample}): sample Ug3\n", sampleUg3)
#print(f"(n_sample = {i_sample}): sample Ug4\n", sampleUg4)
#print(f"(n_sample = {i_sample}): sample HUg1\n", sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n", sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n", sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n", sampleHUg4)
#print(f"(n_sample = {i_sample}): sample En\n", sampleEn)
#print(f"(n_sample = {i_sample}): sample S2\n", sampleS2)
with open(f"./HUg_{i_sample}.csv", "w") as fHUg:
writer = csv.writer(fHUg)
writer.writerows(sampleHUg)
with open(f"./S2Ug_{i_sample}.csv", "w") as fS2Ug:
writer = csv.writer(fS2Ug)
writer.writerows(sampleS2Ug)
with open(f"./Ug_{i_sample}.csv", "w") as fUg:
writer = csv.writer(fUg)
writer.writerows(sampleUg)
with open(f"./En_{i_sample}.csv", "w") as fEn:
writer = csv.writer(fEn)
writer.writerows(sampleEn)
with open(f"./S2_{i_sample}.csv", "w") as fS2:
writer = csv.writer(fS2)
writer.writerows(sampleS2)
return Ephf, S2
def cost_phf_sample(Quket, print_level,
qulacs_hamiltonian, qulacs_hamiltonianZ, qulacs_s2Z,
qulacs_ancZ, coef0_H, coef0_S2, ref, theta_list,
samplelist):
"""Function:
Sample Hamiltonian and S**2 expectation values with PHF and PUCCSD.
Write out the statistics in csv files.
Author(s): Takashi Tsuchimochi
"""
t1 = time.time()
noa = Quket.noa
nob = Quket.nob
nva = Quket.nva
nvb = Quket.nvb
n_electrons = Quket.n_electrons
n_qubit_system = n_qubits
n_qubits = Quket.n_qubits + 1
anc = n_qubit_system
ndim1 = Quket.ndim1
state = QuantumState(n_qubits)
circuit_rhf = set_circuit_rhfZ(n_qubits, n_electrons)
circuit_rhf.update_quantum_state(state)
if ref == "phf":
circuit_uhf = set_circuit_uhfZ(n_qubits, noa, nob, nva, nvb, theta_list)
circuit_uhf.update_quantum_state(state)
print("pHF")
elif ref == "puccsd":
circuit = set_circuit_uccsd(n_qubits, noa, nob, nva, nvb, theta_list,
ndim1)
for i in range(rho):
circuit.update_quantum_state(state)
print("UCCSD")
if print_level > -1:
print("State before projection")
utils.print_state(state, n_qubit_system)
#### Set post-measurement states ####
#poststate0 = state.copy()
#poststate1 = state.copy()
#circuit0 = QuantumCircuit(n_qubits)
#circuit1 = QuantumCircuit(n_qubits)
#### Projection to anc = 0 or anc = 1 ###
#circuit0.add_gate(P0(0))
#circuit1.add_gate(P1(0))
#circuit0.update_quantum_state(poststate0)
#circuit1.update_quantum_state(poststate1)
#### Renormalize each state ###
#norm0 = poststate0.get_squared_norm()
#norm1 = poststate1.get_squared_norm()
#poststate0.normalize(norm0)
#poststate1.normalize(norm1)
### grid loop ###
Ng = 4
beta = [-0.861136311594053, -0.339981043584856,
0.339981043584856, 0.861136311594053]
wg = [0.173927422568724, 0.326072577431273,
0.326072577431273, 0.173927422568724]
Ng = 2
beta = [0.577350269189626, -0.577350269189626]
wg = [0.5, 0.5]
### a list to compute the probability to observe 0 in ancilla qubit
p0_list = np.full(n_qubits, 2)
p0_list[-1] = 0
### Array for <HUg>, <S2Ug>, <Ug>
# samplelist = [10,100,1000,10000,100000,1000000,10000000]
ncyc = 4
prints("", filepath="./log2.txt")
for i_sample in samplelist:
i_sample_x = i_sample
if i_sample == 10000000:
print("OK")
ncyc = ncyc*10
i_sample_x = 1000000
sampleHUg1 = []
sampleHUg2 = []
sampleHUg3 = []
sampleHUg4 = []
sampleS2Ug1 = []
sampleS2Ug2 = []
sampleS2Ug3 = []
sampleS2Ug4 = []
sampleUg1 = []
sampleUg2 = []
sampleUg3 = []
sampleUg4 = []
# sampleEn = []
# sampleS2 = []
sampleHUg = np.zeros((ncyc, Ng))
sampleS2Ug = np.zeros((ncyc, Ng))
sampleUg = np.zeros((ncyc, Ng))
sampleEn = np.zeros((ncyc, 1))
sampleS2 = np.zeros((ncyc, 1))
for icyc in range(ncyc):
prints(f"n_sample = {i_sample_x} ({icyc} / {ncyc})",
filepath="./log2.txt")
HUg = []
S2Ug = []
Ug = []
Ephf = S2 = Norm = 0
for i in range(Ng):
### Copy quantum state of UHF (cannot be done in real device) ###
state_g = QuantumState(n_qubits)
state_g.load(state)
### Construct Ug test
circuit_ug = QuantumCircuit(n_qubits)
### Hadamard on anc
circuit_ug.add_H_gate(anc)
controlled_Ug(circuit_ug, n_qubits, anc, np.arccos(beta[i]))
circuit_ug.add_H_gate(anc)
circuit_ug.update_quantum_state(state_g)
### Set post-measurement states ####
poststate0 = state_g.copy()
poststate1 = state_g.copy()
circuit0 = QuantumCircuit(n_qubits)
circuit1 = QuantumCircuit(n_qubits)
### Projection to anc = 0 or anc = 1 ###
circuit0.add_gate(P0(anc))
circuit1.add_gate(P1(anc))
circuit0.update_quantum_state(poststate0)
circuit1.update_quantum_state(poststate1)
### Renormalize each state ###
norm0 = poststate0.get_squared_norm()
norm1 = poststate1.get_squared_norm()
poststate0.normalize(norm0)
poststate1.normalize(norm1)
### Set ancilla qubit of poststate1 to zero (so that it won't be used) ###
circuit_anc = QuantumCircuit(n_qubits)
circuit_anc.add_X_gate(anc)
circuit_anc.update_quantum_state(poststate1)
print(
test_transition_observable(
state_g, qulacs_hamiltonianZ,
poststate0, poststate1, 100000))
# exit()
### Probabilities for getting 0 and 1 in ancilla qubit ###
p0 = state_g.get_marginal_probability(p0_list)
p1 = 1 - p0
### Compute expectation value <HUg> ###
HUg.append(sample_observable(state_g,
qulacs_hamiltonianZ,
i_sample_x).real)
#HUg.append(adaptive_sample_observable(state_g,
# qulacs_hamiltonianZ,
# i_sample_x).real)
### <S2Ug> ###
S2Ug.append(sample_observable(state_g,
qulacs_s2Z,
i_sample_x).real)
#S2Ug.append(adaptive_sample_observable(state_g,
# qulacs_s2Z,
# i_sample_x).real)
#S2Ug.append(qulacs_s2Z.get_expectation_value(state_g))
#HUg.append(0)
#S2Ug.append(0)
#Ug.append(p0 - p1)
n_term = qulacs_hamiltonianZ.get_term_count()
n_sample_total = i_sample_x * n_term
# in the worst-case scenario,
# Ug is measured as many times as n_sample_total
#(required to evaluate HUg)
Ug.append(sample_observable(state_g,
qulacs_ancZ,
i_sample_x*n_term).real)
#p0_sample = 0
#for j_sample in range(n_sample_total):
# if(p0 > np.random.rand()):
# p0_sample += 1
#Ug.append(2*p0_sample/n_sample_total - 1)
### Norm accumulation ###
Norm += wg[i]*Ug[i]
sampleHUg[icyc, i] = HUg[i]
sampleS2Ug[icyc, i] = S2Ug[i]
sampleUg[icyc, i] = Ug[i]
#print('p0 : ',p0,' p1 : ',p1, ' p0 - p1 : ',p0-p1)
sampleHUg1.append(HUg[0])
sampleHUg2.append(HUg[1])
#sampleHUg3.append(HUg[2])
#sampleHUg4.append(HUg[3])
sampleS2Ug1.append(S2Ug[0])
sampleS2Ug2.append(S2Ug[1])
#sampleS2Ug3.append(S2Ug[2])
#sampleS2Ug4.append(S2Ug[3])
sampleUg1.append(Ug[0])
sampleUg2.append(Ug[1])
#sampleUg3.append(Ug[2])
#sampleUg4.append(Ug[3])
### Energy calculation <HP>/<P> and <S**2P>/<P> ###
Ephf = 0
for i in range(Ng):
Ephf += wg[i]*HUg[i]/Norm
S2 += wg[i]*S2Ug[i]/Norm
# print(" <S**2> = ", S2, '\n')
Ephf += coef0_H
S2 += coef0_S2
sampleEn[icyc, 0] = Ephf
sampleS2[icyc, 0] = S2
# print(" <E[PHF]> (Nsample = ",i_sample,") = ", Ephf)
#print(f"(n_sample = {i_sample}): sample HUg1\n",sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n",sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n",sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n",sampleHUg4)
#print(f"(n_sample = {i_sample}): sample S2Ug1\n",sampleS2Ug1)
#print(f"(n_sample = {i_sample}): sample S2Ug2\n",sampleS2Ug2)
#print(f"(n_sample = {i_sample}): sample S2Ug3\n",sampleS2Ug3)
#print(f"(n_sample = {i_sample}): sample S2Ug4\n",sampleS2Ug4)
#print(f"(n_sample = {i_sample}): sample Ug1\n",sampleUg1)
#print(f"(n_sample = {i_sample}): sample Ug2\n",sampleUg2)
#print(f"(n_sample = {i_sample}): sample Ug3\n",sampleUg3)
#print(f"(n_sample = {i_sample}): sample Ug4\n",sampleUg4)
#print(f"(n_sample = {i_sample}): sample HUg1\n",sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n",sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n",sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n",sampleHUg4)
#print(f"(n_sample = {i_sample}): sample En\n",sampleEn)
#print(f"(n_sample = {i_sample}): sample S2\n",sampleS2)
with open(f"./Ug_{i_sample}.csv", "w") as fUg:
writer = csv.writer(fUg)
writer.writerows(sampleUg)
with open(f"./HUg_{i_sample}.csv", "w") as fHUg:
writer = csv.writer(fHUg)
writer.writerows(sampleHUg)
with open(f"./S2Ug_{i_sample}.csv", "w") as fS2Ug:
writer = csv.writer(fS2Ug)
writer.writerows(sampleS2Ug)
with open(f"./En_{i_sample}.csv", "w") as fEn:
writer = csv.writer(fEn)
writer.writerows(sampleEn)
with open(f"./S2_{i_smaple}.csv", "w") as fS2:
writer = csv.writer(fS2)
writer.writerows(sampleS2)
return Ephf, S2
ite = int(math.pow(2., num_bits * .5))
cu_gate = DenseMatrix(tuple(range(num_bits + 1)), cu_mat)
dist_gate = DenseMatrix(tuple(range(1, num_bits + 1)), dist_mat)
for t in range(ite):
cu_gate.update_quantum_state(state)
print("\nAfter CU Gate:")
show_quantum_state(state)
dist_gate.update_quantum_state(state)
print("\nAfter Dist Gate:")
show_quantum_state(state)
form = "{" + ":0{}b".format(num_bits) + "}"
print("\nProbabilities:")
for x in range(2**num_bits):
testval = [int(b) for b in form.format(x)]
testval.append(2)
val = state.get_marginal_probability(testval[::-1])
print((form + " {}").format(x, val))
print("\nSecret A was:")
print(("{} (" + form + ")").format(secret_a, secret_a))
del state
